Internal models, adaptation, and uncertainty

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2 Internal models, adaptation, and uncertaintyReza ShadmehrJohns Hopkins School of MedicineJoern DiedrichsenSiavash VaziriMaurice SmithPart of the pleasure of doing research on the brain is that you can get pleasure, indeed inspiration, from observing seemingly everything events. I want to show you one such event that has been fascinating for me. Do the book on left hand task.Ali GhazizadehKonrad Koerding

3 Internal models predict the sensory consequences of motor commandsWhat are internal models? The best example of one comes from the oculomotor literature, where there is clear evidence that the brain predicts the sensory consequences of oculomotor commands.Duhamel, Colby, & Goldberg Science 255, (1992)

4 Measured sensory consequencesforceState changeMotor commandsmusclesBody partIntegrationBayesian mixtureSensory systemProprioceptionVisionAuditionMeasured sensory consequencesWhy should the brain predict the sensory consequences of movement? A number of reasons have been given through out the years, but for me the most compelling reason comes from the work of Daniel Wolpert … Despite the fact that the best evidence for IM comes in the oculomotor literature, there has been no direct test of it. To test it, we need to measure estimate of state in three conditions: via the forward model, sensory feedback alone, and when both sources of information are available.Predicted sensory consequencesForward model

7 Measured sensory consequencesWhat are internal models good for?Improve ability to sense the world. By predicting the sensory consequences of motor commands, and then integrating it with the actual sensory feedback, the brain arrives at an estimate that is better than is possible from sensation alone.musclesforceBody partState changeMotor commandsSensory systemProprioceptionVisionAuditionMeasured sensory consequencesIntegrationBayesian mixtureNow you want to transition to adaptation of internal models.Predicted sensory consequencesForward model

10 Kojima et al. (2004) J Neurosci 24:7531.Offline learning: with passage of time and without explicit training, the motor system still appears to learn_++Result 2: Following changes in gain and a period of darkness, monkeys exhibit a “jump” in memory.Kojima et al. (2004) J Neurosci 24:7531.

15 The learner’s view about the cause of motor errors1. Perturbations that can affect the motor plant have multiple time scales. Some perturbations are fast: muscles recover from fatigue quickly. Some perturbations are slow: recovery from disease may be slow.Faster perturbations are more variable (have more noise).Disturbances result in error, which can be observed, but with sensory noise.The problem of learning is one of credit assignment: when I observe a disturbance, what is the time-scale of this perturbation?To solve this problem, the brain must keep a measure of uncertainty about each possible timescale of perturbation.Koerding, Tenenbaum, Shadmehr, unpublished

17 What prediction dissociates the two models?Model 1 (Smith et al.): Error causes changes in multiple adaptive processes. Fast adaptive processes are highly responsive to error, but quickly forget. Slowly adaptive processes respond poorly to error, but retain their changes.Prediction: When actions are performed with zero error, states of the adaptive processes decay, but at different rates.Model 2 (Koerding et al.): Motor system is disturbed by processes that have various timescale (fatigue vs. disease). Credit assignment of error depends on uncertainty regarding what is the timescale of the disturbance.Prediction: When there are actions but the sensory consequences cannot be observed, states decay at various rates, but uncertainty grows. Increased uncertainty encourages learning.

18 Adapting without uncertaintyModel 1: After a period of “darkness”, there will be spontaneous recovery, but rate of re-adaptation will be the same as initial learning.Trial numberSlow stateFast stateTask reversal period“dark” periodre-adaptation-Smith, Ghazizadeh, Shadmehr PLOS Biology 2006

19 Adapting with uncertaintyModel 2: After a period of “darkness”, there will be spontaneous recovery, but the rate of re-adaptation will be faster than initial learning.Bayesian learnerMonkey data from Kojima et al. (2004). Simulations from Koerding, Tenenbaum, Shadmehr, unpublished

20 Sensory deprivation may increase uncertainty, resulting in faster learningMonkeys were trained each day, but between training sessions they put on dark goggles, reducing their ability to sense consequences of their own motor commands.1100020003000Saccade numberDarknessDarknessRobinson et al. J Neurophysiol, in press

21 Adapting with uncertainty: some predictionsSensory deprivation  Faster subsequent rate of learning.Example: A subject that spends a bit of time in the dark will subsequently learn faster than a subject that spends that time with the lights on.Why: In the dark, uncertainty about state of the motor system increases.Longer inter-stimulus interval  Better retention.Example: A subject that trains on n trials with long ITI will show less forgetting than one that trains on the same n trials with short ITI.Why: events that take place spaced in time will be interpreted as having a long timescale.

22 SummaryJoern DiedrichsenSiavash VaziriBy combining the predictions of internal models with sensory measurements, the brain ends up with less noisy estimates of the environment than is possible with either source of information alone.A prediction error causes changes in multiple adaptive systems. Some are highly responsive to error, but rapidly forget. Others are poorly responsive to error but have high retention. This explains savings and spontaneous recovery.Ali GhazizadehMaurice SmithFast and slow adaptive processes arose because disturbances to the motor system have various timescales (fatigue vs. disease). When faced with error, the brain faces a credit assignment problem: what is the timescale of the disturbance? To solve this problem, the brain likely keeps a measure of uncertainty about the timescales.Konrad Koerding